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Tutorial: Bioinformatics Basics
Eric Holloway

DOI: 10.33014/issn.2640-5652.2.2.holloway.1

1 Introduction

Bioinformatics can appear to be a daunting field, since it
combines the complex science of biology with the complex
theory of computer science. However, the basics are sur-
prisingly simple.

Essentially, bioinformatics is the discipline of analyzing
symbol strings. The symbol strings represent DNA, RNA,
and protein sequences, with each symbol representing a
component of the molecule. The strings are analyzed by
extracting the structure within the strings and comparing
the relationships between the strings. The DNA and RNA
strings are composed with four di!erent symbols and pro-
tein strings have twenty di!erent symbols.

2 Genetic Code and Sequence
Translation

The letters of DNA and RNA strings are called nucleotides.

The DNA letters are G (guanine), A (adenine), T
(thymine), C (cytosine).

The RNA letters are G, A, U, C. It is almost directly copied
from DNA, except the T is turned into U (uracil).

The protein letters are A, C, D, E, F, G, H, I, K, L, M, N,
P, Q, R, S, T, V, W, Y. See Figure 1 for their meanings.

Each protein letter is translated from three RNA letters.
Each group of three RNA letters is called a codon. The
translation table is known as the genetic code. The
START and STOP symbols are used to mark where a gene
begins and ends. Figure 1 shows the standard mapping be-
tween codons and amino acids.

A single biological DNA sequence is a molecule, and this
molecule is called a chromosome.

3 Sequencing and Assembly

These symbol strings are digital abstractions, and show how
fundamental the notion of information is to biology. The
process of extracting these abstractions is called sequenc-
ing.

To extract genetic sequences, current technology must first
break a long sequence into many little fragments, usually on
the order of a few hundred nucleotides long or tens of pro-
teins long. Once these fragments are digitized, then they
must be reassembled back into the full genome. This as-
sembly process is very computationally intensive and error
prone. Sometimes the full genome cannot be assembled,
and the best that can be done is to construct larger frag-
ments.

The fragments are known as reads. A collection of frag-
ments is known as a run. When the fragments are pieced
together into a longer fragment, this is known as a con-
tig. If the full genome is put together, this is known as an
assembly. If there are gaps, but the contigs are lined up
against a known genome, this is called a sca!old.

4 Accessing Data

The data for each step in the process is usually stored in
databases, so that experiments and assembly can be repro-
duced by other scientists. The databases described in this
article are the databases maintained by the National Cen-
ter for Biotechnology Information, since these are what the
author is most familiar with.

All items of data in the database have a unique ID number
known as an accession. Once you know the accession for
a piece of data, you can use NCBI’s tools to download the
data. The main tool you can to search for an accession and
download the data is NCBI’s website: www.ncbi.nlm.nih.
gov/.

If you want to access data from the command line for an
automated workflow, there are a couple command line tools
available.

https://dx.doi.org/10.33014/issn.2640-5652.2.2.holloway.1
www.ncbi.nlm.nih.gov/
www.ncbi.nlm.nih.gov/


36 Tutorial: Bioinformatics Basics

Amino Acid Symbol Codon
Alanine A GCU, GCC, GCA, GCG

Asparagine/Aspartic Acid B AAU, AAC, GAU, GAC
Cysteine C UGU, UGC

Aspartic Acid D GAU, GAC
Glutamic Acid E GAA, GAG
Phenylalanine F UUU, UUC

Glycine G GGU, GGC, GGA, GGG
Histidine H CAU, CAC
Isoleucine I AUU, AUC, AUA

Lysine K AAA, AAG
Leucine L CUU, CUC, CUA, CUG, UUA, UUG

Methionine M AUG
Asparagine N AAU, AAC

Proline P CCU, CCC, CCA, CCG
Glutamine Q CAA, CAG
Arginine R CGU, CGC, CGA, CGG, AGA, AGG
Serine S UCU, UCC, UCA, UCG, AGU, AGC

Threonine T ACU, ACC, ACA, ACG
Valine V GUU, GUC, GUA, GUG

Tryptophan W UGG
Tyrosine Y UAU, UAC

Glutamine/Glutamic Acid Z CAA, CAG, GAA, GAG
Start AUG
Stop UAA, UGA, UAG

Figure 1: The Standard Codon Table



Volume 2, Issue 2

5. FINDING THINGS WITH BLAST 37

4.1 File Format

The main file format used for DNA and protein sequences is
the FASTA format. The format is pretty straightforward.
There are metadata lines and lines that contain the genetic
data. The metadata lines start with the > character, and
can be used to break up multiple reads in a run file. The
rest of the lines contain DNA or protein letters. Sometimes,
there are extra letters that stand in for multiple possible
DNA letters. Most often this is the letter N, which means
any DNA letter can go in that particular spot. It signifies
a sequencing error. See the next section for an example of
this file format.

4.2 Sequence Read Archive

The initial read data is stored in NCBI’s Sequence Read
Archive (SRA): www.ncbi.nlm.nih.gov/sra. To access
this data there is the sra-toolkit: github.com/ncbi/sra-
tools.

Here in an example session of using the toolkit to then
download a run.
$ fas tq !dump !!f a s t a !Z DRR001793 | head !n 8
>DRR001793 . 1 FC30UF2AAXX: 6 : 1 : 1 0 : 1 4 5 3 l ength=35
ACCAGCTATCACCGAGTTTNNNTATCCTTTCACCC
>DRR001793 . 2 FC30UF2AAXX: 6 : 1 : 1 0 : 1 6 9 2 l ength=35
TATTATTTAACTGATAATTANNCTAGATATATTAT
>DRR001793 . 3 FC30UF2AAXX: 6 : 1 : 1 0 : 1 8 9 6 l ength=35
AGACCAATTCATTAATTTTTTNTTTATTATACTAT
>DRR001793 . 4 FC30UF2AAXX: 6 : 1 : 1 0 : 1 8 4 5 l ength=35
AAAGGCAGAGTACATTAAGACNATAGATTTAGTTT

You can see from the above the downloaded file consists
of lines of DNA spaced with identifier lines. Each of these
lines of DNA is a read.

4.3 Eutils

The assembled genomes and proteins can be accessed
with the Eutils web API. The documentation for the API
is available at https://www.ncbi.nlm.nih.gov/books/
NBK25500/. There is a Python library available for using
Eutils in Python available at https://pypi.org/project/
eutils/. Additionally, there are also command line tools
for Eutils, and a great tutorial on them is available at
https://www.ncbi.nlm.nih.gov/books/NBK179288/.

The general workflow for Eutils is a query tool returns an
XML report, which is then fed to a data access tool to
retrieve the record.

Eutils is the most versatile tool, since it can access data
from all the di!erent NCBI databases of processed genetic
data.

4.4 Datasets

Finally, NCBI has recently released a beta version of a more
user friendly search and data access tool called Datasets:
ncbi.nlm.nih.gov/datasets. The tool’s main feature is
the ability to download many pieces of data altogether in
a single ’bag’. The idea is to make accessing data like a
grocery shopping store, where you fill your cart with data
items, and then check them all out at once. There is also
a command line tool that can be used for programmatic
workflows.

5 Finding Things With BLAST

BLAST (basic local alignment search tool) is the Google of
bioinformatics, hosted at the National Center for Biotech-
nology Information. You can enter DNA or an amino acid
sequence into the tool, and BLAST will search the NCBI
databases for matches.

Since DNA is mutated—with swapped, deleted, and added
nucleotides—there is never an exact match. BLAST
searches heuristically using a variant of the edit distance
with a substitution matrix, representing the probability one
nucleotide mutates into another nucleotide.

There are four main variants of BLAST, representing
the four possible combinations of DNA and amino acid
searches.

1. blastn: enter DNA sequence to find DNA sequence

2. blastx: enter DNA sequence to find amino acid se-
quence

3. tblastn: enter amino acid sequence to find DNA se-
quence

4. blastp: enter amino acid sequence to find amino acid
sequence

5.1 BLAST Interface

The basic interface is straightforward, and the web inter-
face at https://blast.ncbi.nlm.nih.gov/Blast.cgi is

www.ncbi.nlm.nih.gov/sra
github.com/ncbi/sra-tools
github.com/ncbi/sra-tools
https://www.ncbi.nlm.nih.gov/books/NBK25500/
https://www.ncbi.nlm.nih.gov/books/NBK25500/
https://pypi.org/project/eutils/
https://pypi.org/project/eutils/
https://www.ncbi.nlm.nih.gov/books/NBK179288/
ncbi.nlm.nih.gov/datasets
https://blast.ncbi.nlm.nih.gov/Blast.cgi


38 Tutorial: Bioinformatics Basics

self descriptive. You enter a reference to a sequence, such
as the accession number, or paste the sequence itself. A
specific subrange of the sequence can be queried.

To constrain the search, di!erent database can be searched,
ranging from databases of carefully currated genomes to
user submission databases. Searches can be within specific
organisms, or exclude specific organisms.

The search sensitivity is tuned by setting the expect thresh-
old, which will return only matches that do not exceed the
expected number of random matches.

The results come back with metadata expressing in which
genetic data the match was found, an ’e’ score based on the
logarithm of probability of matching, and what percentage
of the returned sequence matches the query. The text of the
sequence itself is also returned, along with any associated
publications.

5.2 Example: Finding the TOP2A
Protein

The simplest way to find a known protein is through the
NCBI search bar, available from the NCBI front page at
ncbi.nlm.nih.gov. Typing in TOP2A will bring up an
information card, with links to the known orthologs in other
species as well as a prepopulated BLAST search. You can
then run the BLAST search to retrieve matches. Note, you
can only exclude results at the taxonomic level, so if there
is another protein that is similar, it will show up in the
search as well.

6 Summary

As you can see, while the user interface for accessing data
may be a bit complicated, and the naming convention for
various aspects of genetic data obscure, the fundamentals
are straightforward. And, once you know that genetic data
is composed into DNA, RNA, and protein sequences, you
just need to download the data to start analyzing it from
the comfort of your living room. The NCBI website is a
good pathway to becoming a bioinformatics scientist, all
without the need for an expensive lab and fancy equipment.
All you need is a computer with an internet connection and
the mind between your ears.

ncbi.nlm.nih.gov

	About This Journal
	The Purpose of the Journal
	Paper Submission Policies
	Other Journal Content

	From the Editors
	Annie CrawfordAnnie CrawfordMetaphor and Meaning  in the Teleological Language of Biology
	Introduction
	The History of Teleology in the Biological Sciences
	Teleological language is Essential to Biology
	If teleological language is essential to biology, then life must be teleological
	Conclusion: Life All the Way Down

	Salvador CordovaSalvador CordovaFisher's Fundamental Theorem of Natural Selection  Isn't Fundamental After All
	Introduction: The Problem of Defining Fitness
	Absolute ``Darwinian'' Fitness vs. Relative Fitness
	Mean and Variance of Relative Fitnesses
	Numerical Examples to Illustrate Fisher's Theorem
	Discussion
	Conclusion

	Eric HollowayEric HollowayTutorial: Bioinformatics Basics
	Introduction
	Genetic Code and Sequence Translation
	Sequencing and Assembly
	Accessing Data
	Finding Things With BLAST
	Summary

	Letters and Notes
	Eric HollowayYou Cannot Get Meaning From Randomness
	Jonathan BartlettActive Information is a Specified Complexity Model
	Eric HollowayEvolution in the Valley of Illusions
	Jonathan BartlettIs Active Information Applicable to Biology?
	Eric HollowayEmpirical Active Information

	News

